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Updated: Jun 6, 2026

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CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose,

Fanni Zhang1, Kristine Broglio1, Michael Sweeting2

  • 1Oncology Biometrics, AstraZeneca, Gaithersburg, Maryland, USA.

Pharmaceutical Statistics
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for optimizing cancer drug doses by balancing benefits and risks across multiple outcomes. The Clinical Utility Index (CUI) method aids decision-making in early-phase oncology trials.

Keywords:
R shiny appclinical utility indexdose optimizationmultiple endpointsoptimal biological dose

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Dose optimization in oncology trials has evolved from finding maximum tolerated dose to identifying the Optimal Biological Dose (OBD).
  • Existing advanced dose-finding methods may not be suitable for small sample sizes in randomized dose optimization studies with multiple endpoints.
  • There is a need for methods that integrate multiple clinical attributes to balance therapeutic benefits and risks.

Purpose of the Study:

  • To propose a novel Clinical Utility Index (CUI) based analysis and decision framework (CUI-MET) for dose optimization in multiple-dose, multiple-outcome randomized trials.
  • To provide a flexible and accessible solution for informed decision-making in early-phase oncology trials.
  • To integrate patient-relevant outcomes into the dose selection process.

Main Methods:

  • Developed a framework integrating multiple binary endpoints into a combined CUI for each dose level by weighting endpoints.
  • Estimated marginal summaries of individual endpoints empirically or via parametric dose-response models.
  • Utilized bootstrap analysis for confidence intervals and to evaluate the robustness of dose selection. Implemented in an R Shiny application.

Main Results:

  • The CUI-MET framework computes a utility score for each dose by combining estimated endpoint probabilities using weights.
  • The optimal dose is selected as the one with the highest score within a clinically acceptable set.
  • The framework demonstrated flexibility in model selection and endpoint weighting schemes.

Conclusions:

  • CUI-MET offers a flexible and accessible solution for dose optimization in early-phase oncology trials by integrating multiple endpoints into a single utility index.
  • The framework supports informed decision-making by providing confidence intervals and probability estimates for dose selection.
  • User-friendly visualizations enhance usability and facilitate the integration of patient-relevant outcomes.